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20222026
most citedWorDepth: Variational Language Prior for Monocular Depth Estimation

2 citations · 4 across the 8 of their papers we have counts for

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cs.CV2026

Dexterous Tactile World Model

Ziyao Zeng, Xiatao Sun, Hao Wang +6

World models for manipulation are typically trained from video, yet the events that determine how manipulation unfolds, such as making and releasing contact, are difficult to obser…

cs.CV2024

RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions

Ziyao Zeng, Yangchao Wu, Hyoungseob Park +6

We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection du…

cs.CV2024

Iris: Integrating Language into Diffusion-based Monocular Depth Estimation

Ziyao Zeng, Jingcheng Ni, Daniel Wang +5

Traditional monocular depth estimation suffers from inherent ambiguity and visual nuisances. We demonstrate that language can enhance monocular depth estimation by providing an add…

cs.CV2024★ 2 cited

WorDepth: Variational Language Prior for Monocular Depth Estimation

Ziyao Zeng, Daniel Wang, Fengyu Yang +6

Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly…

cs.CV2024

Tactile-Augmented Radiance Fields

Yiming Dou, Fengyu Yang, Yi Liu +2

We present a scene representation, which we call a tactile-augmented radiance field (TaRF), that brings vision and touch into a shared 3D space. This representation can be used to…

cs.CV2024

Binding Touch to Everything: Learning Unified Multimodal Tactile Representations

Fengyu Yang, Chao Feng, Ziyang Chen +8

The ability to associate touch with other modalities has huge implications for humans and computational systems. However, multimodal learning with touch remains challenging due to…

cs.CV2022★ 2 cited

On the Viability of Monocular Depth Pre-training for Semantic Segmentation

Dong Lao, Fengyu Yang, Daniel Wang +4

The question of whether pre-training on geometric tasks is viable for downstream transfer to semantic tasks is important for two reasons, one practical and the other scientific. If…